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--- |
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dataset_info: |
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features: |
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- name: transcript_segment |
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dtype: string |
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- name: prior_context |
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dtype: string |
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- name: prior_speakers |
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dtype: string |
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- name: speaker |
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dtype: string |
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- name: change |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 357571 |
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num_examples: 294 |
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download_size: 179841 |
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dataset_size: 357571 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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license: mit |
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task_categories: |
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- text-generation |
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- question-answering |
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language: |
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- en |
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tags: |
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- finance |
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--- |
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# Transcript Speaker Identification Dataset |
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## Description |
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This dataset is designed to facilitate the development and evaluation of models that identify and assign speakers and speaker changes within event transcripts. It consists of segments from various transcripts where the primary task is to determine who the speaker is, based on the given textual context and a list of possible speakers. |
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The dataset was assembled from three earnings events: |
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- Q4 2023 Amazon.Com Inc Earnings Call |
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- Q2 2024 Apple Inc Earnings Call |
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- Q2 2024 Adobe Inc Earnings Call |
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## Dataset Structure |
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### Columns |
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- `transcript_segment`: A specific segment of the transcript which requires speaker identification. |
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- `prior_context`: The textual context preceding the `transcript_segment`, which can be instrumental in identifying the speaker. |
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- `possible_speakers`: A list of names or identifiers representing individuals who could potentially be the speaker of the segment. |
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- `speaker`: The actual speaker of the `transcript_segment` |
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- `change`: 1 or 0 indicating whether the speaker has changed |
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### Data Format |
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The dataset is presented in a tabular format, where each row corresponds to a data point that includes a transcript segment, its prior context, the possible speakers, the label indicating the actual speaker, and an indicator of whether the speaker has changed between the prior context and the transcript segment. |
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## Use Cases |
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This dataset can be used for a variety of applications, including: |
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- Training machine learning models for speaker identification in transcripts. |
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- Enhancing speech recognition systems by improving their ability to attribute text to the correct speaker. |
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- Developing tools for automated meeting summarization where speaker labels are essential. |
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## Accessing the Dataset |
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You can access this dataset via the HuggingFace Datasets library using the following Python code: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Aiera/aiera-speaker-assign") |
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``` |
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A guide for evaluating using EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) is available on [github](https://github.com/aiera-inc/aiera-benchmark-tasks). |
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